code: def preprocess_function(examples):
      new_examples = {
          "input_ids_j": [],
          "attention_mask_j": [],
          "input_ids_k": [],
          "attention_mask_k": [],
      }
      for question, response_j, response_k in zip(
          examples["question"],
          examples["response_j"],
          examples["response_k"],
      ):
          tokenized_j = tokenizer(
              "Question: " + question + "\n\nAnswer: " + response_j,
              truncation=True,
          )
          tokenized_k = tokenizer(
              "Question: " + question + "\n\nAnswer: " + response_k,
              truncation=True,
          )
  
          new_examples["input_ids_j"].append(tokenized_j["input_ids"])
          new_examples["attention_mask_j"].append(
              tokenized_j["attention_mask"]
          )
          new_examples["input_ids_k"].append(tokenized_k["input_ids"])
          new_examples["attention_mask_k"].append(
              tokenized_k["attention_mask"]
          )
  
      return new_examples
  
file_path: trl/examples/research_projects/stack_llama/scripts/reward_modeling.py
imports: from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Union
  import evaluate
  import numpy
  import torch
  import torch.nn
  from datasets import load_dataset
  from peft import LoraConfig, TaskType, get_peft_model
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser, PreTrainedTokenizerBase, Trainer, TrainerCallback, TrainingArguments
  from transformers.utils import PaddingStrategy
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess_function(examples) and returns: new_examples